期刊
OPTICS EXPRESS
卷 27, 期 16, 页码 23173-23185出版社
Optica Publishing Group
DOI: 10.1364/OE.27.023173
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资金
- National Key Research and Development Program of China [2017YFC0820604]
- National Natural Science Foundation of China (NSFC) [61671196, 61525206, 61701149, 51705404]
- Zhejiang Province Nature Science Foundation of China [LR17F030006]
- China Scholarship Council Foundation (CSC) [201806285004]
- National Natural Science Major Foundation of Research Instrumentation of China [61427808]
- Key Foundation of China [61333009]
- National Key Basic Research Program of China [2012CB821204]
Two-dimensional phase unwrapping algorithms are widely used in optical metrology and measurements. The high noise from interference measurements, however, often leads to the failure of conventional phase unwrapping algorithms. In this paper, we propose a deep convolutional neural network (DCNN) based method to perform rapid and robust two-dimensional phase unwrapping. In our approach, we employ a DCNN architecture, DeepLabV3+, with noise suppression and strong feature representation capabilities. The employed DCNN is first used to perform semantic segmentation to obtain the segmentation result of the wrapped phase map. We then combine the wrapped phase map with the segmentation result to generate the unwrapped phase. We benchmarked our results by comparing them with well-established methods. The reported approach out-performed the conventional path-dependent and path-independent algorithms. We also tested the robustness of the reported approach using interference measurements from optical metrology setups. Our results, again, clearly out-performed the conventional phase unwrap algorithms. The reported approach may find applications in optical metrology and microscopy imaging. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
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